Axonal regeneration and functional recovery are poor after spinal cord injury (SCI), typified by the formation of an injury scar. While this scar was traditionally believed to be primarily ...responsible for axonal regeneration failure, current knowledge takes a more holistic approach that considers the intrinsic growth capacity of axons. Targeting the SCI scar has also not reproducibly yielded nearly the same efficacy in animal models compared to these neuron-directed approaches. These results suggest that the major reason behind central nervous system (CNS) regeneration failure is not the injury scar but a failure to stimulate axon growth adequately. These findings raise questions about whether targeting neuroinflammation and glial scarring still constitute viable translational avenues. We provide a comprehensive review of the dual role of neuroinflammation and scarring after SCI and how future research can produce therapeutic strategies targeting the hurdles to axonal regeneration posed by these processes without compromising neuroprotection.
Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. ...This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management.
We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance.
The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value.
This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.
Introduction: Lung cancer remains a global health concern, with non-small cell lung cancer (NSCLC) comprising the majority of cases. Early detection of lung cancer has led to an increased number of ...cases identified in the earlier stages of NSCLC. This required the revaluation of the NSCLC treatment approaches for early stage NSCLC. Methods: We conducted a comprehensive search using multiple databases to identify relevant studies on treatment modalities for early stage NSCLC. Inclusion criteria prioritized, but were not limited to, clinical trials and meta-analyses on surgical approaches to early stage NSCLC conducted from 2021 onwards. Discussion: Minimally invasive approaches, such as VATS and RATS, along with lung resection techniques, including sublobar resection, have emerged as treatments for early stage NSCLC. Ground-glass opacities (GGOs) have shown prognostic significance, especially when analyzing the consolidation/tumor ratio (CTR). There have also been updates on managing GGOs, including the non-surgical approaches, the extent of lung resection indicated, and the level of lymphadenectomy required. Conclusions: The management of early stage NSCLC requires a further assessment of treatment strategies. This includes understanding the required extent of surgical resection, interpreting the significance of GGOs (specifically GGOs with a high CTR), and evaluating the efficacy of alternative therapies. Customized treatment involving surgical and non-surgical interventions is essential for advancing patient care.
Post-acute COVID-19 sequelae, commonly known as long COVID, encompasses a range of systemic symptoms experienced by a significant number of COVID-19 survivors. The underlying pathophysiology of long ...COVID has become a topic of intense research discussion. While chronic inflammation in long COVID has received considerable attention, the role of neutrophils, which are the most abundant of all immune cells and primary responders to inflammation, has been unfortunately overlooked, perhaps due to their short lifespan. In this review, we discuss the emerging role of neutrophil extracellular traps (NETs) in the persistent inflammatory response observed in long COVID patients. We present early evidence linking the persistence of NETs to pulmonary fibrosis, cardiovascular abnormalities, and neurological dysfunction in long COVID. Several uncertainties require investigation in future studies. These include the mechanisms by which SARS-CoV-2 brings about sustained neutrophil activation phenotypes after infection resolution; whether the heterogeneity of neutrophils seen in acute SARS-CoV-2 infection persists into the chronic phase; whether the presence of autoantibodies in long COVID can induce NETs and protect them from degradation; whether NETs exert differential, organ-specific effects; specifically which NET components contribute to organ-specific pathologies, such as pulmonary fibrosis; and whether senescent cells can drive NET formation through their pro-inflammatory secretome in long COVID. Answering these questions may pave the way for the development of clinically applicable strategies targeting NETs, providing relief for this emerging health crisis.
Cellular senescence is a biological aging hallmark that plays a key role in the development of neurodegenerative diseases. Clinical trials are currently underway to evaluate the effectiveness of ...senotherapies for these diseases. However, the impact of senescence on brain aging and cognitive decline in the absence of neurodegeneration remains uncertain. Moreover, patient populations like cancer survivors, traumatic brain injury survivors, obese individuals, obstructive sleep apnea patients, and chronic kidney disease patients can suffer age-related brain changes like cognitive decline prematurely, suggesting that they may suffer accelerated senescence in the brain. Understanding the role of senescence in neurocognitive deficits linked to these conditions is crucial, especially considering the rapidly evolving field of senotherapeutics. Such treatments could help alleviate early brain aging in these patients, significantly reducing patient morbidity and healthcare costs. This review provides a translational perspective on how cellular senescence plays a role in brain aging and age-related cognitive decline. We also discuss important caveats surrounding mainstream senotherapies like senolytics and senomorphics, and present emerging evidence of hyperbaric oxygen therapy and immune-directed therapies as viable modalities for reducing senescent cell burden.
Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.
Data from the National ...Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.
The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.
This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.
Cerebellar hydatid cysts are uncommon lesions, with limited cases reported in the literature. This systematic review aimed to summarize current diagnostic and management approaches, given the low ...suspicion index of hydatid cysts in the cerebellum. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42023437853. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) reporting guidelines. Two independent researchers searched PubMed, Scopus, and Google Scholar databases on June 27, 2023. We included 15 studies published between 1965 and 2022, comprising 12 case reports and three case series. A pooled analysis of reported cases (nine females and seven males) with cerebellar hydatid cysts revealed a mean age of 24 ± 20 years. Most of the cases were reported in Turkish hospitals (n = 8). The prominent signs and symptoms observed were headaches (10, 62.5%), ataxic gait (9, 56.25%), and visual disturbances (9, 56.25%). The time from symptom onset to hospital visit varied, with most patients seeking medical attention within the first three months. The left cerebellar hemisphere was the most common location of the cysts (6, 37.5%), and compression of the fourth ventricle was frequently observed. Computed tomography (CT) and magnetic resonance imaging (MRI) were the primary diagnostic tools used in three-fourths of cases, and surgical intervention was the primary treatment approach. Albendazole and praziquantel were commonly prescribed postoperatively, and two patients underwent preoperative needle decompression. This systematic review contributes to a better understanding of cerebellar hydatid cysts and guides future research and clinical management of this entity.
Aneurysmal bone cysts (ABCs) are non-neoplastic primary bone tumors, typically involving the long bones and vertebrae in the first 2 decades of life. ABCs require prompt diagnosis and intervention ...due to their rapidly expansile nature and ability to destroy the adjacent normal bone. ABCs rarely affect the rib. We report a case of a 51-year-old female presenting with chronic dry cough and right upper back pain. A chest X-ray and computed tomography scan revealed an expansile, lytic mass affecting the posterior aspect of the third right rib. The third right rib was resected using a posterolateral, Shaw-Paulson approach. Histopathology of the resected mass confirmed the diagnosis of ABC. There were no intra- or perioperative complications, and follow-up X-ray was normal.
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale ...(mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.
